Digital Commons Network^™

Early-Age Strength Monitoring Of Sensor-Embedded 3d Printed Structures, Ali Kazemian, Marwa Hassan, Hassan Noorvand, Gabriel Arce, Kasra Banijamali

Publications

Construction 3D printing (C3DP) is an automated technology that has the potential to reduce the construction time and cost by utilizing different types of robotic systems. Due to the absence of formwork in C3DP, potential excessive water evaporation can cause issues to the freshly-printed structures that are exposed to the ambient curing condition. Moreover, the 3D printed elements should carry the service loads in a short time after printing to provide a load-bearing capacity. Destructive and non-destructive methods are the two types of methods that are being used in the construction industry for quality monitoring. Due to the time-consuming nature …

Personalized Bayesian Inference For Explainable Healthcare Management And Intervention, Utkarshani Jaimini, Krishnaprasad Thirunaravan, Maninder Kalra, Robin Dawson, Amit Sheth

Publications

Chronic healthcare conditions such as Asthma re- quires constant monitoring and managing of symptoms and their triggers for better quality of life. Each asthma patient reacts very differently to potential triggers. Hence, there is a need to develop a explainable personalized framework for each patient to capture susceptibility to asthma triggers. We developed a personalized knowledge-based probabilistic model to predict asthma exacerbation for different environmental factors utilizing patient generated health data from pediatric asthma patients. Further, the personalized model provides a metric, called Health Coefficient, to quantify the health of a patient for varying environmental factors. We demonstrate the predictive …

Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach, Kaushik Roy, Yuxn Zi, Amit Sheth

Publications

Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or “state” across runs or sessions. This helps with referencing past information to make system outputs more contextual and relevant. This position paper proposes a framework for temporal alignment with several configurable toggles. We present four alignment mechanisms: knowledge graph path-based, neural score-based, vector similarity-based, and sequential process-guided alignment. By offering these interchangeable approaches, we aim to provide a flexible solution adaptable to complex and real-world applications. This paper discusses the potential benefits and …

Neurosymbolic Value-Inspired Ai (Why, What, And How), Amit Sheth, Kaushik Roy

Publications

The rapid progression of Artificial Intelligence (AI) systems, facilitated by the advent of Large Language Models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries. This trend has sparked significant discourse centered around the ever-increasing need for LLM-based AI systems to function among humans as part of human society, sharing human values, especially as these systems are deployed in high-stakes settings (e.g., healthcare, autonomous driving, etc.). Towards this end, neurosymbolic AI systems are attractive due to their potential to enable easy-tounderstand and interpretable interfaces for facilitating valuebased decision-making, by leveraging explicit representations of shared values. …

K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur

Publications

Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to tend to a user’s persona appropriately. This is particularly crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response with supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. …

Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth

Publications

Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question answering (QA). However, due to encoding a text-based projection of the world, they have also been shown to lack a fullbodied understanding of such events, e.g., a sense of intuitive physics, and cause-and-effect relationships among events. In this work, we propose using causal event graphs (CEGs) to enhance language understanding of spatiotemporal events in language models, using a novel approach that also provides proofs for the model’s capture of the CEGs. A CEG consists of events denoted by …

Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth

Publications

Despite their wide applications to language understanding tasks, large language models (LLMs) still face challenges such as hallucinations - the occasional fabrication of information, and alignment issues - the lack of associations with human-curated world models (e.g., intuitive physics or common-sense knowledge). Additionally, the black-box nature of LLMs makes it highly challenging to train them meaningfully in order to achieve a desired behavior. Specifically, the attempt to adjust LLMs’ concept embedding spaces can be highly intractable, which involves analyzing the implicit impact on LLMs’ numerous parameters and the resulting inductive biases. This paper proposes a novel architecture that wraps powerful …

Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Causal Neuro-Symbolic Ai: A Synergy Between Causality And Neuro-Symbolic Methods, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

Causal Neuro-Symbolic AI combines the benefits of causality with Neuro-Symbolic Artificial Intelligence (NeSyAI). More specifically, it (1) enriches NeSyAI systems with explicit representations of causality, (2) integrates causal knowledge with domain knowledge, and (3) enables the use of NeSyAI techniques for causal AI tasks. The explicit causal representation yields insights that predictive models may fail to analyze from observational data. It can also assist people in decision-making scenarios where discerning the cause of an outcome is necessary to choose among various interventions.

Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy

Publications

Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance1.

Neurosymbolic Customized And Compact Copilots, Kaushik Roy, Megha Chakraborty, Yuxin Zi, Manas Gaur, Amit Sheth

Publications

Large Language Models (LLMs) are credible with open-domain interactions such as question answering, summarization, and explanation generation [1]. LLM reasoning is based on parametrized knowledge, and as a consequence, the models often produce absurdities and inconsistencies in outputs (e.g., hallucinations and confirmation biases) [2]. In essence, they are fundamentally hard to control to prevent off-the-rails behaviors, are hard to fine-tune, customize for tailored needs, prompt effectively (due to the “tug-of-war” between external and parametric memory), and extremely resource-hungry due to the enormous size of their extensive parametric configurations [3,4]. Thus, significant challenges arise when these models are required to perform …

Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth

Publications

Current Virtual Mental Health Assistants (VMHAs) primarily offer counseling and suggestive care but do not assist with patient diagnosis due to their lack of training in safety-constrained and specialized clinical process knowledge, referred to as ProKnow. In this work, we define ProKnow as an ordered set of information aligned with evidence-based guidelines or categories of conceptual understanding used by domain experts. We also introduce a new dataset of diagnostic conversations guided by safety constraints and Pro- Know, known as ProKnow-data. We develop a method for natural language question generation (NLG) designed to interactively gather diagnostic information from patients, termed ProKnow-algo. …

A Comprehensive Survey On Rare Event Prediction, Chathurangi Shyalika Jayakody Kankanamalage, Ruwan Wickramarachchi, Amit Sheth

Publications

Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistics and ML. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, …

Bridging Biomedical Research With Aerospace Engineering In The Lab Of Tomorrow, Christine Walck

Publications

“My goal with this lab is to take biomedical research and tie it into a school that is grounded in aerospace and aeronautical engineering,” says Assistant Professor of Mechanical Engineering at Embry-Riddle Aeronautical University’s Daytona Beach Campus, Christine Walck. “How do I make that connection? That’s the question I’ve been working on for the past couple of years with Vicon.”

Advancements In Ai-Driven Education: Transforming Learning And Training With Intelligent Technologies, Sharon L. Burton

Publications

This article examines the transformative role of Artificial Intelligence (AI) in higher education, focusing on its integration in learning methodologies and its impact on student engagement and outcomes. Recent research underscores the increasing use of AI in education, where it is reshaping teaching and learning dynamics by facilitating personalized and effective learning experiences. Applying Literature review, the study further explores AI's role in enhancing independent learning abilities and student satisfaction, with intelligent learning tools proving crucial in improving user contentment and intent to use. Additionally, it considers the ethical and risk management aspects of AI in education, including data governance …

Fundamentals Of Sustainability, Eva Maleviti

Publications

Fundamentals of Sustainable Aviation is the frst textbook to survey the critical feld of sustainability within the aviation industry. Taking a systems thinking approach, it presents the foundational principles of sustainability and methodically applies them to diferent aviation sectors. Opening with the basics of sustainability, emphasising the Sustainable Development Goals, the book then considers the environmental, economic and social dimensions of aviation. The following chapters apply these insights to aviation design, supply chains, operations, maintenance and facilities. The fnal chapter examines the concept of resilience in sustainable aviation. Overall, the textbook shows how future sustainability can be achieved by making …

Using Student Experience Reports As A Window For Teacher Reflections On Inquiry Science Instructional Practices, Jessie Nixon, Mimi Recker

Publications

This paper examines how student experience reports, collected as exit tickets, can be leveraged during professional learning to support science teachers’ understanding of their students and inform future classroom practices. Our analysis frames exit ticket data as a boundary object, which bridges across researchers’, teachers’, and students’ perspectives of the learning environment. During two successive professional learning activities, teachers examined and reflected upon their students’ exit ticket data. A thematic analysis of teachers’ discourse during these activities distilled two themes. The first theme surfaced how exit data helped teachers form new views about their students’ emerging science identities; the second …

Elara Conference - Techniques & Technologies To Support Sustainable Education In The Academic Sphere Year Ii Book 2, Volume 2, European Common Laboratory For Pedagogical Action-Research And Student-Centred Learning (Elara)

Publications

The Fourth Industrial Revolution was and s till is focused on digitalising processes and using AI to increas e productivity. Industry 5.0, on the other hand, focuses on the human factor and the human as the centre of the production process. According to the new paradigm, technology should benefit humans, and human s, in turn, should actively collaborate with machines. In other words, while Industry 4.0 wa s based on the relationship between machines and IT systems, Industry 5.0 brings together the strengths of humans and machines. Following this train of thought, we have come up with the idea to …

Elara Conference - Techniques & Technologies To Support Sustainable Education In The Academic Sphere Year Ii Book 2 Vol 1, European Common Laboratory For Pedagogical Action-Research And Student-Centred Learning (Elara)

Publications

The Fourth Industrial Revolution was and s till is focused on digitalising processes and using AI to increase productivity. Industry 5.0, on the other hand, focuses on the human factor and the human as the centre of the production process. According to the new paradigm, technology should benefit humans, and human s, in turn, should actively collaborate with machines. In other words, while Industry 4.0 wa s based on the relationship between machines and IT systems, Industry 5.0 brings together the strengths of humans and machines. Following this train of thought, we have come up with the idea to initiate …

Book Review: Wapsipinicon Almanac: Selections From Thirty Years, Andy Oler

Publications

The Wapsipinicon Almanac: Selections from Thirty Years, edited by Timothy Fay. Iowa City: University of Iowa press, 2023. xi, 249 pp. Illustrations. $27.50 paperback.

Preempting The Buzz: Challenges For State And Local Regulation Of Drone Noise, Daniel Friedenzohn, Trevor Simoneau

Publications

From carrying life-saving medical supplies to delivering Amazon packages, drones are increasingly playing a more significant role in our society. Such operations, however, raise a variety of legal and public policy questions, all requiring answers before drone deliveries become commonplace in the package delivery ecosystem. One legal issue that remains significantly underexplored is the challenge of regulating drone noise. This issue raises vital questions of federalism and preemption in the broader debate over federal versus state and local power to govern different aspects of civilian drone operations.

Even though the noise generated by drones is much quieter than other types …

Validating The Critical Teaching Behaviors Midterm Feedback Instrument For Enhanced Teaching Effectiveness And Student Engagement, Claudia Cornejo Happel, Chad Rohrbacher, Teha Cooks, Jenna Korentsides, Joseph R. Keebler, Carmen Van Ommen, Kimberly N. Williams, Lauren Barbeau

Publications

This study, conducted at a private STEM university in the Southeastern United States, presents a series of qualitative and quantitative data validating the Critical Teaching Behaviors Midterm Feedback Instrument (CTB-MFI). The instrument’s validity and reliability were tested through a mixed-methods approach involving faculty surveys, statistical analyses, and a student experience survey and focus groups. A confirmatory factor analysis (CFA) suggests that the CTB-MFI model has excellent fit across multiple indicators, as well as excellent convergent validity. However, low discriminant validity indicates possible correlations between categories of critical teaching behaviors. Data further show that faculty and students perceive the instrument as …

Dynamics Of A 3-D Inlet/Isolator Measured With Fast Pressure-Sensitive Paint, Andrew N. Bustard, Mark E. Noftz, Mitsugu Hasegawa, Hirotaka Sakaue, Joseph S. Jewell, Nicholas J. Bisek, Thomas J. Juliano

Publications

Fast pressure-sensitive paint (PSP) was applied to an inlet/isolator designed using the Osculating Internal Waverider Inlet with Parallel Streamlines (OIWPS) method. The dorsal isolator surface pressure was measured using anodized-aluminum PSP through transparent cast acrylic that makes up the ventral portion of the isolator. Temperature-sensitive paint was utilized to correct for the PSP’s temperature sensitivity. The model was tested under Mach 5.7 flow at Re = 8.5 ×106 /m and 10.2 ×106 /m in the AFOSR–Notre Dame Large Mach-6 Quiet Tunnel (ANDLM6QT) under conventional noise conditions. Flow phenomena, such as shocks originating in the inlet and flow separation at the …

Short-Term Forecasting Airport Passenger Flow During Periods Of Volatility: Comparative Investigation Of Time Series Vs. Neural Network Models, David H. Hopfe, Kiljae Lee, Chunyan Yu

Publications

Recurrent Neural Networks (RNNs), known for handling complex data tasks like language translation and speech recognition, are seldom employed in airport management practice for daily and weekly passenger flow forecasting tasks. In this paper, we evaluate the effectiveness and adaptability of various neural network models (RNN, LSTM, GRU, Deep LSTM, Bidirectional LSTM, multivariate RNN, and multivariate LSTM) against standard time series models (ARIMA, SARIMA, and SARIMAX) for a short-term forecasting airport security checkpoint passenger flows at five major U.S. airports during the pandemic.

At Atlanta's Hartsfield-Jackson Airport (ATL), the RNN notably surpasses SARIMA's forecasting accuracy by 34% (DM= 3.44, p …

Exploring The Nexus Of Cybersecurity Leadership, Human Factors, Emotional Intelligence, Innovative Work Behavior, And Critical Leadership Traits, Sharon L. Burton, Darrell Norman Burrell, Calvin Nobles, Laura A. Jones

Publications

Data shows that 12% of leaders are rated as 'very effective' at leadership. This research emphasizes the importance of understanding human behavior and its impact on leadership effectiveness, innovative work behavior (IWB), and the ability to respond to complex cyber threats, particularly in the realm of cybersecurity leadership. Emotional intelligence (EI), a key human factor, is highlighted as a crucial element that can stimulate cognitive absorption, leading to innovative work behavior and improved innovation efficiency (IE). This underscores the need for leaders to not only be technically proficient but also emotionally intelligent to effectively manage their teams and respond to …

Barriers To The Adoption Of Digitalization In The Construction Industry: Perspectives Of Owners, Contractors, And Contractors, Waleed Abu-Khader

Publications

Construction organizations are moving toward adopting digitalization in response to Industry 4.0. However, the slow adoption of digitalization has been observed. This study aimed to assess the level of digitalization adoption and evaluate the barriers to adopting digitalization in the Jordanian construction industry by all project parties in the public and private sectors. The study targets were achieved through a structured questionnaire using SPSS software for analysis through the following statistical tests: Normality test, Mann-Whitney U, Kruskal-Wallis H, and Exploratory Factor Analysis (EFA). Most respondents used software at the design stage, and less than half did not use any software …

Impact Of Weather Factors On Airport Arrival Rates: Application Of Machine Learning In Air Transportation, Robert W. Maxson, Dothang Truong, Woojin Choi

Publications

Weather is responsible for approximately 70% of air transportation delays in the National Airspace System, and delays resulting from convective weather alone cost airlines and passengers millions of dollars each year due to delays that could be avoided. This research sought to establish relationships between environmental variables and airport efficiency estimates by data mining archived weather and airport performance data at ten geographically and climatologically different airports. Several meaningful relationships were discovered from six out of ten airports using various machine learning methods within an overarching data mining protocol, and the developed models were tested using historical data.

Multi-Layer Evolution Of Acoustic-Gravity Waves And Ionospheric Disturbances Over The United States After The 2022 Hunga Tonga Volcano Eruption, P. A. Inchin, A. Bhatt, S. A. Cummer, S. D. Eckermann, B. J. Harding, J. Ma, J. J. Makela, J B. Snively

Publications

e Hunga-Tonga Hunga-Ha'apai volcano underwent a series of large-magnitude eruptions that generated in the atmosphere. We investigate the spatial and temporal evolutions of fluctuations driven by atmospheric acoustic-gravity waves (AGWs) and, in particular, the Lamb wave modes in high spatial resolution data sets measured over the Continental United States (CONUS), complemented with data over the Americas and the Pacific. Along with >800 barometer sites, tropospheric observations, and Total Electron Content data from >3,000 receivers, we report detections of volcano-induced AGWs in mesopause and ionosphere-thermosphere airglow imagery and Fabry-Perot interferometry. We also report unique AGW signatures in the ionospheric D-region, measured …

The Life Cycle Of Stars And Their Planets From The High Energy Perspective, Lia Corrales, Keivan G. Stassun, Tim Cunningham, Girish M. Duvvuri, Jeremy J. Drake, Catherine Espaillat, Adina D. Feinstein, Elena Gallo, Hans Moritz Günther, George W. King, Marina Kounkel, Carey M. Lisse, Rodolfo Montez Jr., David A. Principe, Jesús A. Toalá, Scott J. Wolk, Raven Cilley, Tansu Daylan, Margarita Karovska, Pragati Pradhan, Peter J. Wheatley, Jun Yang

Publications

One of the key research themes identified by the Astro2020 decadal survey is Worlds and Suns in Context. The Advanced X-ray Imaging Satellite (AXIS) is a proposed NASA APEX mission that will become the prime high-energy instrument for studying star-planet connections from birth to death. This work explores the major advances in this broad domain of research that will be enabled by the AXIS mission, through X-ray observations of stars in clusters spanning a broad range of ages, flaring M-dwarf stars known to host exoplanets, and young stars exhibiting accretion interactions with their protoplanetary disks. In addition, we explore the …

An Ontology Design Pattern For Representing Causality, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

The causal pattern is a proposed ontology design pattern for representing the structure of causal relations in a knowledge graph. This pattern is grounded in the concepts defined and used by the CausalAI community i.e., Causal Bayesian Networks and do-calculus. Specifically, the pattern models three primary concepts: (1) causal relations, (2) causal event roles, and (3) causal effect weights. Two use cases involving a sprinkler system and asthma patients are provided along with their relevant competency questions.